State of the art reservation systems are normally based on dedicated Global Distribution Systems (GDS), as for example airlines reservation systems which provide flight search applications for shopping business like flight booking. This activity, also called “shopping for booking” involves a lot of computation and may take some time. To minimize this delay, users usually have few degrees of freedom: they must specify origin and destination cities, outbound and inbound dates of the journey. To further minimize the delay, they may specify e.g. a preferred operating carrier and cabin class if the have precise flights requirements. Users search for the best price for a particular travel (city pair, departure and arrival date) in the aim of booking it eventually. The search usually offers some flexibility: e.g. returning the 100 cheapest flights recommendations for the requested travel; returning cheaper flights for closely-related dates. All needed computation (searching the cheapest fares and rules combination, checking seat availability of candidate flights . . . ) are performed at the time of the query, which ensures that the returned recommendations will be available for booking. Consequently, such search transactions are costly and take several seconds to complete. This cost precludes them from answering more open search requests, such as for instances the cheapest flight for the coming two or three months. While this is advantageous for system performance and for response times, it is not ideal for users who would certainly appreciate a more user friendly interaction with wider freedom in the parameters choice.
A different approach to the task of searching air travel prices is the so called “pre-shopping”. With this term we refer to those activities which require interrogations of data bases through a reservation system but which do not necessarily result in a proper booking. This activities are of key importance for airlines or travel agencies, because, even if they do not generate an immediate revenue they can influence the future choice of their potential customers. It would be highly appreciated a tool able to provide a zero-delay response to a user's query with many degrees of freedom. With pre-shopping, users can browse a carrier or travel agency's entire catalogue of air travels. Those users wish to make their mind prior to shopping by browsing recommendations over billions of travels recommendations. Compared to shopping, browsing recommendations implies instantaneous responses to searches (few tens of milliseconds). The typical approach of pre-shopping systems is thus to let users browse a cache of pre-computed travel recommendations. With such approach, the search queries can be much more powerful: users can search on many open criteria: origin city only, range of dates, range of price . . . . For the sake of the example:                “Search for 2 or 3 weeks trips from Paris to any destination within the next 12 month below 600 Euros”        
The drawback of this approach is that the recommendations returned to users are only guaranteed to be valid at the time of their pre-computation. In particular, they may no longer be eligible for booking at the time of the search.
Unlike other cache browsing domains (e.g. WWW search), air travel pre-shopping is very sensible to air travel price volatility: the best prices of flights in the coming weeks is likely to change every day. This volatility greatly impacts the cache accuracy, i.e., the consistency between price in pre-shopping and price in shopping. The usual accuracy rate in the industry is about 20-30%.
Maintaining higher cache accuracy often means massive re-computations (to deal with the entire catalogue of travel) and also frequent re-computations (to deal with flight volatility). This is very demanding in hardware resources.
State of the art pre-shopping tools have some drawbacks which limit the efficiency of the tool. For example TravelTainment pre-shopping platform: (“TTibe: TravelTainment Internet Booking Engine” http://www.traveltainment.fr/a-propos-de-traveltainment/qui-sommes-nous/) provides browsing facilities over its own database of pre-computed travels (flights departing from German cities mainly). The air travel data are provided e.g. by Amadeus' Extreme Pricer, a product of Massive Computation Platform (MCP). Travel data represent the cheapest flights from several thousands of city pairs, for every day of the coming year, for all stay durations between 1 and 23 days. Every day, the entire base of travel (several tens of millions of prices) is recomputed by Amadeus and sent to TravelTainment for integration into their platform. While the travel domain is rather exhaustive from its customers' standpoint, this approach has two main drawbacks:                All data are recomputed by Amadeus every day, which has an operational cost        Integrating this amount of data is costly for TravelTainment and can only be performed once a day. This has an impact on price accuracy experienced by their customers.        
Other commercially available platforms are Kayak's Explore (http://wvvvv.kayak.com/news/kayak-adds-map-based-search-tool-to-popular-ipad-app.bd.html),
Opodo's EscapeMap:
(http://promos.opodo.co.uk/airtools/escape_map.html),
Lufthansa's Trip Finder:
(http://www.lufthansa.com/online/portal/lh/us/nonav/local?nodeid=3322431&1=en) which is powered by Amadeus technologies. These three pre-shopping platforms have a different strategy than TravelTainment to feed their cache of pre-computed solution: they all rely on recording real shopping traffic, i.e. record the result of search transaction operated on their shopping platform. This approach has an advantage in that the pre-computation comes at almost no cost. However it comes with a series of penalties for their respective customers:                unusual destinations might not be available to pre-shopping due to recorded traffic;        there are a lot of “holes” in the price domains, due to missing dates in recorded traffic        it is difficult to propose complex travel recommendations, for example advance purchase        some volatile price recommendations are not updated for days (even weeks).        
All these disadvantages can compromise to a great extent the pre-shopping accuracy experienced by the customers.